About Peft
PEFT is a parameter-efficient fine-tuning library from Hugging Face built for adapting large language models with less compute and smaller model updates. Instead of fully retraining a base model, it centers on techniques that modify a limited subset of parameters, which can make experimentation and deployment more practical for teams working with constrained GPU budgets. For LLM use cases, that matters because fine-tuning costs can grow quickly as model size increases. PEFT is most relevant for practitioners who want a flexible way to customize foundation models for domain tasks, instruction tuning, or downstream applications while keeping training runs lighter than full-model tuning. Its appeal comes from its focus: efficient adaptation rather than end-to-end model training infrastructure. Teams already working in the Hugging Face ecosystem may find it especially useful as a focused layer in a broader fine-tuning stack.